Multimodal Affective Analysis Using Hierarchical Attention Strategy with Word-Level Alignment (P18-1)
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| Challenge: | Existing approaches to classify human affect and subjective information from multiple data sources are limited by the lack of high-level feature associations. |
| Approach: | They propose a hierarchical multimodal architecture with attention and word-level fusion to classify utterance-level sentiment and emotion from text and audio data. |
| Outcome: | The proposed model outperforms state-of-the-art approaches on published datasets and visualizes and interprets synchronized attention over modalities. |
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